Validate Data

Validate pending migrations for foreign key consistency, rollback safety, and best practices

Systematische Erkundung und Test einer Webanwendung zur Fehlerfindung, Aufdeckung von UX-Problemen und anderen Schwierigkeiten. Verwenden Sie dies, wenn Sie aufgefordert werden, eine Anwendung zu "dogfooden", "QA-Tests durchzuführen", "explorativ zu testen", "Probleme zu finden", "nach Fehlern zu suchen" oder die Qualität einer Webanwendung zu überprüfen. Erzeugt einen strukturierten Bericht mit vollständigen Reproduktionsnachweisen – Schritt-für-Schritt-Screenshots, Repro-Videos und detaillierte Repro-Schritte für jedes Problem –, damit die Erkenntnisse direkt an die zuständigen Teams weitergegeben werden können.

Design and execute insect population surveys covering survey design, sampling methods, field execution, specimen identification, diversity index calculation including Shannon-Wiener and Simpson indices, statistical analysis, and reporting. Covers defining survey objectives, selecting study sites, determining sampling intensity and replication, choosing sampling methods appropriate to target taxa, standardizing collection effort, recording environmental covariates, identifying specimens to the lowest practical taxonomic level, calculating species richness, Shannon-Wiener diversity (H'), Simpson diversity (1-D), evenness, rarefaction curves, multivariate ordination, and producing survey reports with species lists and conservation implications. Use when conducting baseline biodiversity assessments, monitoring insect populations over time, comparing insect communities across habitats or treatments, assessing environmental impact, or supporting conservation planning with quantitative ecological data.

Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.